




"""simple docstring"""







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE_	     =       16
SCREAMING_SNAKE_CASE_	     =       32





def      __snake_case (				_lowercase			,_lowercase			,_lowercase			,_lowercase			,_lowercase = 16    ):


    """simple docstring"""







    UpperCamelCase              =	AutoTokenizer.from_pretrained('''bert-base-cased'''    )
    UpperCamelCase              =	DatasetDict(
        {
            '''train''': dataset['''train'''].select(_lowercase    ),
            '''validation''': dataset['''train'''].select(_lowercase    ),
            '''test''': dataset['''validation'''],
        }    )

    def tokenize_function(_lowercase    ):
        # max_length=None => use the model max length (it's actually the default)
        UpperCamelCase              =	tokenizer(examples['''sentence1''']			,examples['''sentence2''']			,truncation=_lowercase			,max_length=_lowercase    )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        UpperCamelCase              =	datasets.map(
            _lowercase			,batched=_lowercase			,remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    UpperCamelCase              =	tokenized_datasets.rename_column('''label'''			,'''labels'''    )

    def collate_fn(_lowercase    ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        UpperCamelCase              =	128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            UpperCamelCase              =	16
        elif accelerator.mixed_precision != "no":
            UpperCamelCase              =	8
        else:
            UpperCamelCase              =	None

        return tokenizer.pad(
            _lowercase			,padding='''longest'''			,max_length=_lowercase			,pad_to_multiple_of=_lowercase			,return_tensors='''pt'''			,)

    # Instantiate dataloaders.
    UpperCamelCase              =	DataLoader(
        tokenized_datasets['''train''']			,shuffle=_lowercase			,collate_fn=_lowercase			,batch_size=_lowercase    )
    UpperCamelCase              =	DataLoader(
        tokenized_datasets['''validation''']			,shuffle=_lowercase			,collate_fn=_lowercase			,batch_size=_lowercase    )

    UpperCamelCase              =	DataLoader(
        tokenized_datasets['''test''']			,shuffle=_lowercase			,collate_fn=_lowercase			,batch_size=_lowercase    )

    return train_dataloader, eval_dataloader, test_dataloader





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	[]
    # Download the dataset
    UpperCamelCase              =	load_dataset('''glue'''			,'''mrpc'''    )
    # Create our splits
    UpperCamelCase              =	StratifiedKFold(n_splits=int(args.num_folds    )    )
    # Initialize accelerator
    UpperCamelCase              =	Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    UpperCamelCase              =	config['''lr''']
    UpperCamelCase              =	int(config['''num_epochs''']    )
    UpperCamelCase              =	int(config['''seed''']    )
    UpperCamelCase              =	int(config['''batch_size''']    )

    UpperCamelCase              =	evaluate.load('''glue'''			,'''mrpc'''    )

    # If the batch size is too big we use gradient accumulation
    UpperCamelCase              =	1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        UpperCamelCase              =	batch_size // MAX_GPU_BATCH_SIZE
        UpperCamelCase              =	MAX_GPU_BATCH_SIZE

    set_seed(_lowercase    )

    # New Code #
    # Create our folds:
    UpperCamelCase              =	kfold.split(np.zeros(datasets['''train'''].num_rows    )			,datasets['''train''']['''label''']    )
    UpperCamelCase              =	[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(_lowercase    ):
        UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase              =	get_fold_dataloaders(
            _lowercase			,_lowercase			,_lowercase			,_lowercase			,)
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        UpperCamelCase              =	AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,return_dict=_lowercase    )

        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
        UpperCamelCase              =	model.to(accelerator.device    )

        # Instantiate optimizer
        UpperCamelCase              =	AdamW(params=model.parameters()			,lr=_lowercase    )

        # Instantiate scheduler
        UpperCamelCase              =	get_linear_schedule_with_warmup(
            optimizer=_lowercase			,num_warmup_steps=100			,num_training_steps=(len(_lowercase    ) * num_epochs) // gradient_accumulation_steps			,)

        # Prepare everything
        # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
        # prepare method.
        UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase              =	accelerator.prepare(
            _lowercase			,_lowercase			,_lowercase			,_lowercase			,_lowercase    )

        # Now we train the model
        for epoch in range(_lowercase    ):
            model.train()
            for step, batch in enumerate(_lowercase    ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device    )
                UpperCamelCase              =	model(**_lowercase    )
                UpperCamelCase              =	outputs.loss
                UpperCamelCase              =	loss / gradient_accumulation_steps
                accelerator.backward(_lowercase    )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(_lowercase    ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device    )
                with torch.no_grad():
                    UpperCamelCase              =	model(**_lowercase    )
                UpperCamelCase              =	outputs.logits.argmax(dim=-1    )
                UpperCamelCase       ,	UpperCamelCase              =	accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
                metric.add_batch(
                    predictions=_lowercase			,references=_lowercase			,)

            UpperCamelCase              =	metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:'			,_lowercase    )

        # New Code #
        # We also run predictions on the test set at the very end
        UpperCamelCase              =	[]
        for step, batch in enumerate(_lowercase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            with torch.no_grad():
                UpperCamelCase              =	model(**_lowercase    )
            UpperCamelCase              =	outputs.logits
            UpperCamelCase       ,	UpperCamelCase              =	accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
            fold_predictions.append(predictions.cpu()    )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()    )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(_lowercase			,dim=0    )    )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    UpperCamelCase              =	torch.cat(_lowercase			,dim=0    )
    UpperCamelCase              =	torch.stack(_lowercase			,dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
    UpperCamelCase              =	metric.compute(predictions=_lowercase			,references=_lowercase    )
    accelerator.print('''Average test metrics from all folds:'''			,_lowercase    )





def      __snake_case (				):


    """simple docstring"""







    UpperCamelCase              =	argparse.ArgumentParser(description='''Simple example of training script.'''    )
    parser.add_argument(
        '''--mixed_precision'''			,type=_lowercase			,default=_lowercase			,choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,help='''Whether to use mixed precision. Choose'''
        '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
        '''and an Nvidia Ampere GPU.'''			,)
    parser.add_argument('''--cpu'''			,action='''store_true'''			,help='''If passed, will train on the CPU.'''    )
    # New Code #
    parser.add_argument('''--num_folds'''			,type=_lowercase			,default=3			,help='''The number of splits to perform across the dataset'''    )
    UpperCamelCase              =	parser.parse_args()
    UpperCamelCase              =	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(_lowercase			,_lowercase    )


if __name__ == "__main__":
  main()

"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :						List[Any]       		=		logging.get_logger(__name__)

a       :						str       		=		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					'camembert'







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]=3_05_22  ,       __UpperCamelCase      :							Tuple=7_68  ,       __UpperCamelCase      :							Optional[Any]=12  ,       __UpperCamelCase      :							int=12  ,       __UpperCamelCase      :							int=30_72  ,       __UpperCamelCase      :							int="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							List[Any]=5_12  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Dict=0.0_2  ,       __UpperCamelCase      :							Optional[int]=1e-12  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							int=0  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Optional[int]="absolute"  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							List[str]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       bos_token_id=__UpperCamelCase  ,       eos_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = position_embedding_type
        _UpperCAmelCase        = use_cache
        _UpperCAmelCase        = classifier_dropout







class 		a_    (			_UpperCAmelCase						):







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase        = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase        = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )